To optimize an obstacle recognition algorithm for point cloud data, a large amount of correctly labeled point cloud data is needed to serve as training samples. In addition, to verify the effect of the recognition algorithm, a large amount of correctly labeled point cloud data is also needed to verify the recognition result. Therefore, a large amount of point cloud data in different scenarios needs to be acquired, and correctly labeled, to facilitate training of the algorithm and verification of the recognition result. To ensure the correctness of the sample data, the labeling process is generally implemented as an entirely manual process in the prior art.
However, in a point cloud image, the point cloud is shown as points in a three-dimensional space, and surrounding object characteristics are sometimes not intuitive or obvious. Meanwhile, further affected by the ground and other roadside sundries, the object characteristics are relatively difficult to be recognized with human eyes and easily cause visual fatigue, resulting in low labeling efficiency.